Systems and methods for identifying patterns in blockchain activities

ABSTRACT

Systems and methods for identifying patterns in blockchain activities based on multi-modal data using artificial intelligence models that compensate for training data featuring a high proportion of missing data points. For example, the system may receive blockchain activity record data for a plurality of blockchain activities involving a plurality of blockchain accounts. The system may input the data into an artificial intelligence model, wherein the artificial intelligence model is trained to identify serial relationships of related blockchain activities corresponding to inputted target blockchain activities based on proportions of digital assets at subsets of blockchain accounts of the plurality of blockchain accounts. The system may receive an output from the artificial intelligence model. The system may generate for display, in a user interface, a visualization of the target blockchain activity based on the output.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims the benefit of priority of U.S. ProvisionalApplication No. 63/329,054, filed Apr. 8, 2022. The content of theforegoing application is incorporated herein in its entirety byreference.

BACKGROUND

A blockchain is a distributed database that is shared among the nodes ofa computer network. The blockchain stores information electronically ina digital format as “blocks” in the blockchain. The proliferation ofblockchain technology has led to an increase in the use of severalapplications of this technology such as cryptocurrencies, smartcontracts, and decentralized applications. Cryptocurrency is a virtualor digital currency exchanged through a computer network that is notreliant on any central authority, such as a government or bank, touphold or maintain it. Smart contracts are computer programs stored on ablockchain that run when predetermined conditions are met. Smartcontracts are used to automate the execution of an agreement so that allparticipants can be immediately certain of the outcome, without anyintermediary's involvement or time loss. Smart contracts can alsoautomate a workflow, triggering the next action when conditions are met.Finally, a decentralized application (“dApp”) is a type of distributedopen-source software application that runs on a peer-to-peer (“P2P”)blockchain network rather than on a single computer.

A notable characteristic of blockchain technology, and its effect on theaforementioned applications, is that it functions without a centralauthority. That is, blockchains and blockchain technology aredecentralized such that facilitation, management, and/or verification ofblockchain-based operations is governed or administered not by any oneauthority but instead by a community of users. The blockchain maytherefore remain distributed (e.g., on a network of computers thatcommunicate and coordinate their actions by passing messages to oneanother), and in many cases public, through a digital ledger, whichrecords the series of blocks forming the chain. Notably, because eachblock depends on a preceding block, edits to existing blocks in thechain may not be made without affecting subsequent blocks, thus leadingto blockchain records being immutable.

In many instances, to use the aforementioned applications, as well asblockchain technology in general, users are required to use specializedcomputer programs that are linked to digital wallets that allow users tostore, manage, and trade their cryptocurrencies. However, as blockchaintechnology does not rely on a central authority and interactions betweenblockchain-based applications are linked to the digital wallet and notan identity of a user behind the interaction, blockchain technology maybe used to mask the identity of a user or entity behind theinteractions. As such, blockchain technology has the potential to beused for illegal, malicious, and/or fraudulent activities, with littlerecourse to (or knowledge of) the users or entities behind theinteractions, as well as fully understanding the scope and nature of theactivities themselves.

SUMMARY

Systems and methods are described herein for novel uses and/orimprovements to blockchains and blockchain technology, particularly inorder to address, but not limited to, the technical problems describedabove. For example, the systems and methods may relate to monitoring,determining, and/or facilitating discovery of blockchain activities,users/entities involved in blockchain activities, a nature or scope ofblockchain activities, an intent of a blockchain activity, and/or anyother information related to one or more blockchain activities.

As one example, methods and systems are described herein for the use ofartificial intelligence to detect patterns in blockchain data to providethe aforementioned technical benefits. Broadly described, artificialintelligence refers to a wide-ranging branch of computer scienceconcerned with building smart machines capable of performing tasks thattypically require human intelligence. Key benefits of artificialintelligence are its ability to process data, find underlying patterns,and/or perform real-time determinations. However, despite these benefitsand despite the wide-ranging number of potential applications, practicalimplementations of artificial intelligence have been hindered by severaltechnical problems. First, artificial intelligence often relies on largeamounts of high-quality data. The process for obtaining this data andensuring it is high-quality is often complex and time-consuming. Second,despite the mainstream popularity of artificial intelligence, practicalimplementations of artificial intelligence require specialized knowledgeto design, program, and integrate artificial intelligence-basedsolutions, which limits the amount of people and resources available tocreate these practical implementations. Finally, results based onartificial intelligence are notoriously difficult to review as theprocess by which the results are made may be unknown or obscured. Thisobscurity creates hurdles for identifying errors in the results, as wellas improving the models providing the results. These technical problemspresent an inherent problem with attempting to use an artificialintelligence-based solution in identifying patterns in blockchainactivities.

Furthermore, the detection of illegal, malicious, and/or fraudulentblockchain activities presents a unique challenge compared with thedetection of other events because the detection of a these blockchainactivities requires an immediate response. For example, if a fraudulentblockchain activity is detected, a system must take immediate action toprevent the fraud. If not, a user may be left with little recourse dueto the immutable nature of blockchain activities. Thus, the system mustact quickly, accurately, and with a high amount of precision.

However, the use of artificial intelligence for this application has afundamental flaw that presents a unique technical challenge; namely,artificial intelligence, whether based on machine learning, deeplearning, etc., requires ample and high-quality training data to train amodel to make accurate and precise determinations. Such training datadoes not exist for blockchain activities. Furthermore, as blockchainactivities are linked to digital wallets as opposed to user identities,it is difficult to obtain any training data for use in monitoring,determining, and/or facilitating discovery of blockchain activities,users/entities involved in blockchain activities, a nature or scope of ablockchain activities, an intent of a blockchain activity, and/or anyother information related to one or more blockchain activities.

To overcome these technical deficiencies in conventional systems,systems and methods disclosed herein identify patterns in blockchainactivities based on multi-modal data using artificial intelligencemodels that compensate for training data featuring a high proportion ofmissing data points. For example, the missing data points may compriseintermediary blockchain activity and/or unknown entities related toblockchain accounts. Furthermore, many blockchain activities involvefungible digital assets that are passed through numerous intermediaryaccounts as part of an exponentially large pool of blockchain activitydata. Specifically, the system is trained on proportions of digitalassets at subsets of blockchain accounts of the plurality of blockchainaccounts.

For example, the system may use the proportions of digital assets at agiven blockchain account as a metric for determining whether or not thegiven blockchain account is related to a target blockchain activity.Notably, the target blockchain activity may include a series ofintermediary activities. These intermediary activities may comprisetransactions of digital assets that are either indistinguishable fromother digital assets by their nature (e.g., the assets are fungible) ormay be indistinguishable due to the volume of related and/or unrelatedblockchain activities occurring. However, the system may identifypatterns in transactions by monitoring the balance of digital assets ata given account and determining based on that balance whether or not atransfer of digital assets corresponding to the target blockchainactivity has occurred. By doing so, the system does not need to generatetraining data or track individual, intermediary blockchain activities.By instead training the model at the aggregate level, the system mayovercome the problem of a high proportion of missing data points.

Furthermore, by monitoring blockchain activities at this aggregatedlevel and only for intermediary blockchain activities that are relevant,the system may generate visualizations of this data that is more easilydigestible for a user. For example, the system may retrieve blockchainactivity record data from a blockchain node. The blockchain record datamay comprise blockchain activities and information about the blockchainactivities (e.g., such as information about the interactions betweenaccounts, addresses, and contracts/programs involved in thetransactions). The system may retrieve this information directly from ablockchain node or may retrieve this information from an indexingservice (e.g., that previously retrieved and/or indexed the data).

In some aspects, systems and methods for identifying patterns inblockchain activities based on multi-modal data using artificialintelligence models that compensate for training data featuring a highproportion of missing data points are described. For example, the systemmay receive blockchain activity record data for a plurality ofblockchain activities involving a plurality of blockchain accounts. Thesystem may receive a first user input selecting a target blockchainactivity. The system may generate a feature input based on theblockchain activity record data and the target blockchain activity. Thesystem may input the feature input into an artificial intelligencemodel, wherein the artificial intelligence model is trained to identifyserial relationships of related blockchain activities corresponding toinputted target blockchain activities based on proportions of digitalassets at subsets of blockchain accounts of the plurality of blockchainaccounts. The system may receive an output from the artificialintelligence model. The system may generate for display, in a userinterface, a visualization of the target blockchain activity based onthe output, wherein the visualization comprises a subset of serialblockchain activities that correspond to the target blockchain activity.

Various other aspects, features, and advantages of the invention will beapparent through the detailed description of the invention and thedrawings attached hereto. It is also to be understood that both theforegoing general description and the following detailed description areexamples and are not restrictive of the scope of the invention. As usedin the specification and in the claims, the singular forms of “a,” “an,”and “the” include plural referents unless the context clearly dictatesotherwise. In addition, as used in the specification and the claims, theterm “or” means “and/or” unless the context clearly dictates otherwise.Additionally, as used in the specification, “a portion” refers to a partof, or the entirety of (i.e., the entire portion), a given item (e.g.,data) unless the context clearly dictates otherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A-B shows an illustrative diagram for identifying patterns inblockchain activities, in accordance with one or more embodiments.

FIG. 2 shows an illustrative diagram for an intelligence service, inaccordance with one or more embodiments.

FIG. 3 shows an illustrative diagram for a model for an intelligenceservice, in accordance with one or more embodiments.

FIG. 4 shows a flowchart of the steps involved identifying patterns inblockchain activities, in accordance with one or more embodiments.

DETAILED DESCRIPTION OF THE DRAWINGS

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the embodiments of the invention. It will beappreciated, however, by those having skill in the art that theembodiments of the invention may be practiced without these specificdetails or with an equivalent arrangement. In other cases, well-knownstructures and devices are shown in block diagram form in order to avoidunnecessarily obscuring the embodiments of the invention.

As stated above, systems and methods are described herein for novel usesand/or improvements to blockchains and blockchain technology,particularly in order to address, but not limited to, the technicalproblems described above. For example, the systems and methods mayrelate to monitoring, determining, and/or facilitating discovery ofblockchain activities, users/entities involved in blockchain activities,a nature or scope of blockchain activities, and/or any other informationrelated to one or more blockchain activities.

For example, the systems and methods may relate to blockchainintelligence, which may include Know-Your-Transaction (KYT) orKnow-Your-Customer (KYC) applications, for blockchain activities. Asreferred to herein, “a blockchain activity” may comprise any activityincluding and/or related to blockchains and blockchain technology. Forexample, blockchain activities may include conducting transactions,querying a distributed ledger, generating additional blocks for ablockchain, transmitting communications-related nonfungible tokens,performing encryption/decryption, exchanging public/private keys, and/orother activities related to blockchains and blockchain technology. Insome embodiments, a blockchain activity may comprise the creation,modification, detection, and/or execution of a smart contract or programstored on a blockchain. In some embodiments, a blockchain activity maycomprise the creation, modification, exchange, and/or review of a token(e.g., a digital blockchain-specific asset), including a nonfungibletoken. A nonfungible token may comprise a token that is associated witha good, a service, a smart contract, and/or other content that may beverified by, and stored using, blockchain technology. As referred toherein, “content” should be understood to mean an electronicallyconsumable user asset, representations of goods or services (includingnonfungible tokens), Internet content (e.g., streaming content,downloadable content, webcasts, etc.), video data, audio data, imagedata, and/or textual data, etc.

KYT refers to monitoring, detecting, and/or facilitating discovery ofblockchain activities, a nature or scope of a blockchain activities,and/or any other information related to one or more blockchainactivities. Similarly, KYC refers to monitoring, determining, and/orfacilitating discovery of users/entities involved in blockchainactivities. In many instances, KYT and KYC applications may beintermingled with each other and an embodiment featuring one should betaken to equally apply to the other. It should be further noted thatmany embodiments rely on implementation involving both blockchaintechnology as well as artificial intelligence. As referred to herein,artificial intelligence (or simply “intelligence”) may include machinelearning, deep learning, computer learning and/or other techniques.Furthermore, artificial intelligence models (or simply “models”) mayinclude machine learning models, deep learning models, etc.

For example, blockchain intelligence applications may include acombination of KYT or KYC intelligence, an easy-to-use interface, and/ora real-time application programming interface (API) for collecting data(including blockchain and non-blockchain data) related to, orinformative of, one or more blockchain activities. For example,blockchain intelligence applications, as described herein, may enableusers working with, or trading in cryptocurrency, to comply with localand global regulations and to reduce manual work processes.

As one example, blockchain intelligence applications may detect patternsof risky activities (e.g., darknet markets, scams, sanctioned addresses,and/or deviating transactions). In addition, the real-time APIs for theblockchain intelligence applications may prevent money withdrawals fromblacklisted addresses and freeze deposits from hacks, scams, and/orransomware. Furthermore, the real-time APIs may ingest data fromnumerous sources, as well as data on conditions that relate toblockchain intelligence and/or rules or recommendations based onblockchain intelligence (e.g., what recommendation is generated for ablockchain activity may depend on what Anti-Money Laundering (“AML”)policy that is applied).

In another example, blockchain intelligence applications mayinvestigate, analyze, and/or link cryptocurrency transactions in theblockchain to real entities and perform monitoring thereon. In suchcases, the blockchain intelligence applications may pull data fromnumerous sources. For example, by entering a cryptocurrency address andautomatically searching through social media forums and darknet sites,the blockchain intelligence applications may identify an entity thatmanages a digital wallet from which a blockchain activity originatedand/or traversed. As referred to herein, “a digital wallet” may comprisea repository that allows users to store, manage, and trade theircryptocurrencies and assets, interact with blockchains, and/or conductblockchain activities using one or more applications. The digital walletmay be specific to a given blockchain protocol or may provide access tomultiple blockchain protocols. In some embodiments, the system may usevarious types of wallets, such as hot wallets and cold wallets. Hotwallets are connected to the Internet while cold wallets are not.

The systems and methods utilizing these blockchain intelligenceapplications may further generate recommendations and/or visualizationson a user interface. For example, the system may generate (or generatefor display on a device) a recommendation that may provide an option toperform an action (or not perform an action), provide a likelihood ofcertainty about an aspect of a blockchain activity, and/or may provideinformation about results of the blockchain intelligence. In anotherexample, the system may generate (or generate for display on a device) avisualization of cryptocurrencies through an intuitive interface thatmay provide insights into a blockchain activity that was initiallyconfusing.

As referred to herein, a “user interface” may comprise a mechanism forhuman-computer interaction and communication in a device and may includedisplay screens, keyboards, a mouse, and the appearance of a desktop.For example, a user interface may comprise a way a user interacts withan application or website in order to generate visualizations of thetarget blockchain activity, and the user interface may display contentrelated to the target blockchain activity.

FIG. 1A-B shows an illustrative diagram for identifying patterns inblockchain activities, in accordance with one or more embodiments. Forexample, FIG. 1 shows diagram 100, which describes a blockchainactivity. As shown, the blockchain activity may comprise a transactionfrom a first blockchain account (e.g., blockchain account 102) to asecond blockchain account (e.g., blockchain account 108). However,included within the blockchain activity is a subset of serial blockchainactivities. This subset of serial blockchain activities includes anintermediary blockchain activity. For example, as shown in diagram 100,within the broader blockchain activity from blockchain account 102 toblockchain account 104, there is a blockchain activity occurring atblockchain account 104. The presence of the intermediary blockchainactivity makes identifying a pattern of a blockchain activity (e.g., atransfer of digital assets from blockchain account 102 to blockchainaccount 104) difficult.

For example, the intermediary blockchain activity may correspond to adifferent entity (e.g., entity 112) than the entity corresponding toblockchain account 104 (e.g., entity 110). Furthermore, the intermediaryblockchain activity may comprise different types of activities and/oraccounts. For example, the system may detect that a blockchain activitycorresponds to an activity or account for collecting a network fee,platform fee, etc., as opposed to a blockchain activity or accountcorresponding to a transfer between relevant entities.

In response, the system may filter out these types of blockchainactivities. Filtering out these types of blockchain activities, not onlyeliminates unnecessary blockchain activities from having to be processby the model, but also eliminates bias from any predictions that may bebased on these unnecessary blockchain activities.

The system may then generate a visualization of a given blockchainactivity (e.g., the blockchain activity of diagram 100) in a format thatis intuitive to a user. Furthermore, the system may filter availabledata to show information relevant to only a target blockchain activity.As referred to herein, the target blockchain activity may comprise ablockchain activity meeting certain criteria. For example, a blockchainactivity that corresponds to a particular transaction, token, entity,digital asset amount, etc.

For example, the visualization may comprise a text description relatedto the target blockchain activity, account, and/or entity. Additionally,or alternatively, the visualization may include a graphicalrepresentation of the subset of serial blockchain activities thatcorrespond to the target blockchain activity, account, and/or entity. Bydoing so, the system may generate a representation of the subset ofserial blockchain activities that correspond to the target blockchainactivity, account, and/or entity that would otherwise be obscured andunintuitive to a user.

In some embodiments, the visualization may include a plurality of iconscorresponding to different intermediary blockchain activities that formthe target blockchain activity. The system may allow users to selectthese icons (e.g., via user interface 212 (FIG. 2 )) in order todetermine additional information related to blockchain accounts,entities, and/or blockchain activities (e.g., an amount of digitalassets involved).

FIG. 2 shows an illustrative diagram for a blockchain intelligenceservice, in accordance with one or more embodiments. For example, insome embodiments, the system may use intelligence service 200 togenerate visualizations of the target blockchain activity. Intelligenceservice 200 may fetch raw data (e.g., data related to a current stateand/or instance of blockchain 202) from a node of a blockchain network(e.g., as described above). Intelligence service 200 may alternativelyor additionally fetch raw data (e.g., data related to other information)from data source 204 (e.g., a non-blockchain source). The system maymonitor and track information from multiple data sources to develop auser and/or entity profile.

The system may monitor content generated by the user to generate userprofile data. As referred to herein, “an entity profile” and/or “entityprofile data” may comprise data actively and/or passively collectedabout an entity. For example, the entity profile data may comprisecontent generated by the entity and an entity characteristic for theentity. An entity profile may be content consumed and/or created by anentity.

Entity profile data may also include an entity characteristic. Asreferred to herein, “an entity characteristic” may include informationabout an entity and/or information included in a directory of storedentity settings, preferences, and information for the entity. Forexample, information about an entity may include historical data onblockchain activities. Additionally, or alternatively, the informationmay include information about entities potentially linked to anotherentity. For example, an entity profile may have information about thesettings for installed programs and operating systems, social mediainformation and/or accounts, financial records, etc. In someembodiments, the entity profile may be a visual display of personal dataassociated with a specific entity. In some embodiments, the entityprofile may be digital representation of an entity's identity. The datain the entity profile may be generated based on the system actively orpassively monitoring.

Intelligence service 200 may then process the data and store it in adatabase and/or data structure in an efficient way to provide quickaccess to the data. For example, intelligence database 206 may publishand/or record a subset of blockchain activities that occur forblockchain 202. Accordingly, for subsequent blockchain activities,intelligence service 200 may reference the index at intelligencedatabase 204 as opposed to a node of blockchain 202 to provide variousservices at user device 210.

For example, intelligence database 206 may store a predetermined list ofblockchain activities to monitor for and/or record in an index. Thesemay include blockchain activities (e.g., “operation included,”“operation removed,” “operation finalized”) related to a given type ofblockchain activity (e.g., “transaction,” “external transfer,” “internaltransfer,” “new contract metadata,” “ownership change,” etc.) as well asblockchain activities related to a given protocol, protocol subgroup,and/or other characteristic (e.g., “ETH,” “ERC20,” and/or “ERC721”).Additionally, and/or alternatively, the various blockchain activitiesand metadata related to those blockchain activities (e.g., blockdesignations, user accounts, time stamps, etc.), as well as an aggregateof multiple blockchain activities (e.g., total blockchain activitiesamounts, rates of blockchain activities, rates of blockchain updates,etc.) may be monitored and/or recorded.

Intelligence database 206 may likewise provide navigation and searchfeatures (e.g., support Boolean operations) for the indexed blockchainactivities on user device 210 (e.g., in user interface 212). In someembodiments, intelligence database 206 may apply one or more formattingprotocols to generate representations of indexed blockchain activitiesin a human-readable format. In some embodiments, intelligence database206 may also tag blockchain activities based on whether or not theblockchain activity originated for a local user account (e.g., a useraccount corresponding to a custodial account) and/or a locally hosteddigital wallet. Intelligence service 200 may determine whether ablockchain activity contains relevant information for users ofintelligence service 200 by storing information about whether an addressis an internal address of intelligence service 200 or one used in adigital wallet hosted by a predetermined wallet service.

System 200 may also include layer 208, which may comprise one or moreAPIs and/or Application Binary Interfaces (“ABIs”). In some embodiments,layer 208 may be implemented on user device 402. Alternatively, oradditionally, layer 208 may reside on one or more cloud components. Forexample, layer 208 may reside on a server and comprise a platformservice for a custodial wallet service, decentralized application, etc.Layer 208 (which may be a REST or web services API layer) may provide adecoupled interface to data and/or functionality of one or moreapplications.

Layer 208 may provide various low-level and/or blockchain-specificoperations in order to facilitate identifying patterns in blockchainactivity. For example, layer 208 may provide blockchain activities suchas blockchain writes. Furthermore, layer 208 may perform a transfervalidation ahead of forwarding the blockchain activity (e.g., atransaction) to another service (e.g., a crypto service). Layer 208 maythen log the outcome. For example, by logging to the blockchain prior toforwarding, the layer 208 may maintain internal records and balanceswithout relying on external verification (e.g., which may take up to tenminutes based on blockchain updating activity).

Layer 208 may also provide informational reads. For example, layer 208(or a platform service powered by layer 208) may generate blockchainactivity logs and write to an additional ledger (e.g., an internalrecord and/or indexer service) the outcome of the reads. If this isdone, a user accessing the information through other means may seeconsistent information such that downstream users ingest the same datapoint as the user.

Layer 208 may also provide a unified API to access balances, transactionhistories, and/or other blockchain activities between one or moredecentralized applications and custodial user accounts. By doing so, thesystem maintains the security of sensitive information such as thebalances and transaction history. Alternatively, a mechanism formaintaining such security would separate the API access between thedecentralized applications and custodial user accounts through the useof special logic. The introduction of the special logic decreases thestreamlining of the system, which may result in system errors based ondivergence and reconciliation.

Layer 208 may provide a common, language-agnostic way of interactingwith an application. In some embodiments, layer 208 may comprise a webservices API that offers a well-defined contract that describes theservices in terms of their operations and the data types used toexchange information. REST APIs do not typically have this contract;instead, they are documented with client libraries for most commonlanguages including Ruby, Java, PHP, and JavaScript. SOAP web serviceshave traditionally been adopted in the enterprise for publishinginternal services, as well as for exchanging information with partnersin business-to-business (B2B) transactions.

Layer 208 may use various architectural arrangements. For example,system 200 may be partially based on layer 208, such that there isstrong adoption of SOAP and RESTful web services, using resources suchas Service Repository and Developer Portal, but with low governance,standardization, and separation of concerns. Alternatively, system 200may be fully based on layer 208, such that separation of concernsbetween layers such as Layer 208, services, and applications are inplace.

In some embodiments, the system architecture may use a microserviceapproach. Such systems may use two types of layers: front-end layers andback-end layers, where microservices reside. In this kind ofarchitecture, the role of the layer 208 may be to provide integrationbetween front-end and back-end layers. In such cases, layer 208 may useRESTful APIs (exposition to front-end or even communication betweenmicroservices). Layer 208 may use the Advanced Message Queuing Protocol(AMQP), which is an open standard for passing business messages betweenapplications or organizations. Layer 208 may use an open-source,high-performance remote procedure call (RPC) framework that may run in adecentralized application environment. In some embodiments, the systemarchitecture may use an open API approach. In such cases, layer 208 mayuse commercial or open-source API platforms and their modules. Layer 208may use a developer portal. Layer 208 may use strong securityconstraints applying a web application firewall that protects thedecentralized applications and/or layer 208 against common web exploits,bots, and denial-of-service (DDoS) attacks. Layer 208 may use RESTfulAPIs as standard for external integration.

As shown in FIG. 2 , system 200 may use layer 208 to communicate withand/or facilitate blockchain activities with user device 210 and/orother cloud components. In some embodiments, the system may also use oneor more ABIs. An ABI is an interface between two program modules, oftenbetween operating systems and user programs. ABIs may be specific to ablockchain protocol. For example, an Ethereum Virtual Machine (EVM) is acore component of the Ethereum network, and a smart contract may be apiece of code stored on the Ethereum blockchain, which are executed onEVM. Smart contracts written in high-level languages like Solidity orVyper may be compiled in EVM executable bytecode by the system. Upondeployment of the smart contract, the bytecode is stored on theblockchain and is associated with an address. To access functionsdefined in high-level languages, the system translates names andarguments into byte representations for byte code to work with it. Tointerpret the bytes sent in response, the system converts back to thetuple (e.g., a finite ordered list of elements) of return values definedin higher-level languages. Languages that compile for the EVM maintainstrict conventions about these conversions, but in order to performthem, the system must maintain the precise names and types associatedwith the operations. The ABI documents these names and types precisely,and in an easily parseable format, making translations betweenhuman-intended method calls and smart contract operations discoverableand reliable.

For example, ABI defines the methods and structures used to interactwith the binary contract similar to an API, but on a lower-level. TheABI indicates the caller of the function to encode (e.g., ABI encoding)the needed information like function signatures and variabledeclarations in a format that the EVM can understand to call thatfunction in bytecode. ABI encoding may be automated by the system usingcompilers or wallets interacting with the blockchain.

As shown in FIG. 2 , system 200 may include one or more user devices(e.g., user device 210). For example, system 200 may comprise adistributed state machine, in which each of the components in FIG. 2acts as a client of system 200. For example, system 200 (as well asother systems described herein) may comprise a large data structure thatholds not only all accounts and balances but also a state machine, whichcan change from block to block according to a predefined set of rulesand which can execute arbitrary machine code. The specific rules ofchanging state from block to block may be maintained by a virtualmachine (e.g., a computer file implemented on and/or accessible by auser device, which behaves like an actual computer) for the system.

It should be noted that user device 210 may comprise any type ofcomputing device, including, but not limited to, a laptop computer, atablet computer, a hand-held computer, and/or other computing equipment(e.g., a server), including “smart,” wireless, wearable, and/or mobiledevices. It should be noted that embodiments describing system 200performing a blockchain activity may equally be applied to, andcorrespond to, an individual user device (e.g., user device 210)performing the blockchain activity. That is, system 200 may correspondto user device (e.g., user device 210) collectively or individually.

In some embodiments, system 200 may represent a decentralizedapplication environment. A decentralized application may comprise anapplication that exists on a blockchain (e.g., blockchain 202) and/or apeer-to-peer network. That is, a decentralized application may comprisean application that has a back-end that is in part powered by adecentralized peer-to-peer network such as a decentralized, open-sourceblockchain with smart contract functionality.

For example, the network may allow user devices (e.g., user device 210)within the network to share files and access. In particular, thepeer-to-peer architecture of the network allows blockchain activities(e.g., corresponding to blockchain 202) to be conducted between the userdevices in the network, without the need of any intermediaries orcentral authorities.

In some embodiments, the user devices of system 200 may comprise one ormore cloud components. For example, cloud components may be implementedas a cloud computing system and may feature one or more componentdevices. It should also be noted that system 200 is not limited to oneuser devise (e.g., user device 210). Users may, for instance, utilizeone or more devices to interact with one another, one or more servers,or other components of system 3200. It should be further noted thatwhile one or more operations (e.g., blockchain activities) are describedherein as being performed by a particular component (e.g., user device304) of system 300, those operations may, in some embodiments, beperformed by other components of system 200. As an example, while one ormore operations are described herein as being performed by components ofuser device 210, those operations may, in some embodiments, be performedby one or more cloud components. In some embodiments, the variouscomputers and systems described herein may include one or more computingdevices that are programmed to perform the described functions.Additionally, or alternatively, multiple users may interact with system200 and/or one or more components of system 200.

With respect to the components of system 200, each of these devices mayreceive content and data via input/output (“I/O”) paths using I/Ocircuitry. Each of these devices may also include processors and/orcontrol circuitry to send and receive commands, requests, and othersuitable data using the I/O paths. The control circuitry may compriseany suitable processing, storage, and/or I/O circuitry. Each of thesedevices may also include a user input interface and/or user outputinterface (e.g., a display such as user interface 212) for use inreceiving and displaying data.

Additionally, the devices in system 200 may run an application (oranother suitable program). The application may cause the processorsand/or control circuitry to perform operations related to identifyingpatterns within blockchain activity within a decentralized applicationenvironment.

Each of these devices may also include electronic storages. Theelectronic storages may include non-transitory storage media thatelectronically stores information. The electronic storage media of theelectronic storages may include one or both of (i) system storage thatis provided integrally (e.g., is substantially non-removable) withservers or client devices, or (ii) removable storage that is removablyconnectable to the servers or client devices via, for example, a port(e.g., a USB port, a firewire port, etc.) or a drive (e.g., a diskdrive, etc.). The electronic storages may include one or more opticallyreadable storage media (e.g., optical disk, etc.), magnetically readablestorage media (e.g., magnetic tape, magnetic hard drive, floppy drive,etc.), electrical charge-based storage media (e.g., EEPROM, RAM, etc.),solid-state storage media (e.g., flash drive, etc.), and/or otherelectronically readable storage media. The electronic storages mayinclude one or more virtual storage resources (e.g., cloud storage, avirtual private network, and/or other virtual storage resources). Theelectronic storages may store software algorithms, informationdetermined by the processors, information obtained from servers,information obtained from client devices, or other information thatenables the functionality as described herein.

System 200 may also use one or more communication paths between devicesand/or components as shown in FIG. 2 . The communication paths mayinclude the Internet, a mobile phone network, a mobile voice or datanetwork (e.g., a 5G or LTE network), a cable network, a public switchedtelephone network, or other types of communication networks orcombinations of communication networks. The communication paths mayseparately or together include one or more communication paths, such asa satellite path, a fiber-optic path, a cable path, a path that supportsInternet communications (e.g., IPTV), free-space connections (e.g., forbroadcast or other wireless signals), or any other suitable wired orwireless communication path or combination of such paths. The computingdevices may include additional communication paths linking a pluralityof hardware, software, and/or firmware components operating together.For example, the computing devices may be implemented by a cloud ofcomputing platforms operating together as the computing devices.

FIG. 3 shows illustrative components for a system for identifyingpatterns in blockchain activities based on multi-modal data (e.g., dataof numerous types and/or from different sources), in accordance with oneor more embodiments. For example, FIG. 3 may show illustrativecomponents for generating visualizations of target blockchain activity.Cloud components 310 may include model 302, which may be a machinelearning model, artificial intelligence model, etc. (which may becollectively referred to herein as “models”). Model 302 may take inputs304 and provide outputs 306. The inputs may include multiple datasets,such as a training dataset and a test dataset. Each of the plurality ofdatasets (e.g., inputs 304) may include data subsets related to userdata, predicted forecasts and/or errors, and/or actual forecasts and/orerrors. For example, in some embodiments, the system may train a modelon a plurality of data types. For example, the system may deriverelevant/significant entities of a blockchain transaction, as well asderive the intent of the blockchain transaction sender by using acombination of, but not limited to: net balance changes of fungibleassets for all parties involved; net balance changes of nonfungibleassets for all parties involved; accounts, addresses, andcontracts/programs involved in the transaction, interactions betweenaccounts, addresses, and contracts/programs involved in the transaction;public data about entities involved; and proprietary data about partiesinvolved. For example, the system may generate feature inputs ofcharacteristics of a known entity, a known blockchain activity, and/oran intermediary blockchain activity of a target blockchain activity inorder to train the model.

In some embodiments, outputs 306 may be fed back to model 302 as inputto train model 302 (e.g., alone or in conjunction with user indicationsof the accuracy of outputs 306, labels associated with the inputs, orwith other reference feedback information). For example, the system mayreceive a first labeled feature input, wherein the first labeled featureinput is labeled with a known prediction for the first labeled featureinput. The system may then train the first machine learning model toclassify the first labeled feature input with the known prediction(e.g., a known entity, a known blockchain activity, an intermediaryblockchain activity of a target blockchain activity, a characteristic ofa blockchain activity, etc.).

In a variety of embodiments, model 302 may update its configurations(e.g., weights, biases, or other parameters) based on the assessment ofits prediction (e.g., outputs 306) and reference feedback information(e.g., user indication of accuracy, reference labels, or otherinformation). In a variety of embodiments, where model 302 is a neuralnetwork, connection weights may be adjusted to reconcile differencesbetween the neural network's prediction and reference feedback. In afurther use case, one or more neurons (or nodes) of the neural networkmay require that their respective errors are sent backward through theneural network to facilitate the update process (e.g., backpropagationof error). Updates to the connection weights may, for example, bereflective of the magnitude of error propagated backward after a forwardpass has been completed. In this way, for example, the model 302 may betrained to generate better predictions.

In some embodiments, model 302 may include an artificial neural network.In such embodiments, model 302 may include an input layer and one ormore hidden layers. Each neural unit of model 302 may be connected withmany other neural units of model 302. Such connections can be enforcingor inhibitory in their effect on the activation state of connectedneural units. In some embodiments, each individual neural unit may havea summation function that combines the values of all of its inputs. Insome embodiments, each connection (or the neural unit itself) may have athreshold function such that the signal must surpass it before itpropagates to other neural units. Model 302 may be self-learning andtrained, rather than explicitly programmed, and can performsignificantly better in certain areas of problem solving, as compared totraditional computer programs. During training, an output layer of model302 may correspond to a classification of model 302, and an input knownto correspond to that classification may be input into an input layer ofmodel 302 during training. During testing, an input without a knownclassification may be input into the input layer, and a determinedclassification may be output.

In some embodiments, model 302 may include multiple layers (e.g., wherea signal path traverses from front layers to back layers). In someembodiments, back propagation techniques may be utilized by model 302where forward stimulation is used to reset weights on the “front” neuralunits. In some embodiments, stimulation and inhibition for model 302 maybe more free-flowing, with connections interacting in a more chaotic andcomplex fashion. During testing, an output layer of model 302 mayindicate whether or not a given input corresponds to a classification ofmodel 302 (e.g., a known entity, a known blockchain activity, anintermediary blockchain activity of a target blockchain activity, acharacteristic of a blockchain activity, etc.).

In some embodiments, the model (e.g., model 302) may automaticallyperform actions based on outputs 306. In some embodiments, the model(e.g., model 302) may not perform any actions. The output of the model(e.g., model 302) may be used to generate a visualization.

FIG. 4 shows a flowchart of the steps involved identifying patterns inblockchain activities, in accordance with one or more embodiments. Forexample, the system may use process 400 (e.g., as implemented on one ormore system components described above) in order to identify patterns inblockchain activities based on multi-modal data using artificialintelligence models that compensate for training data featuring a highproportion of missing data points.

At step 402, process 400 (e.g., using one or more components describedabove) receives blockchain activity record data. For example, the systemmay receive blockchain activity record data for a plurality ofblockchain activities involving a plurality of blockchain accounts. Forexample, the system may retrieve blockchain activity record data from ablockchain node. The blockchain record data may comprise blockchainactivities and information about the blockchain activities (e.g., suchas information about the interactions between accounts, addresses, andcontracts/programs involved in the transactions). The system mayretrieve this information directly from a blockchain node or mayretrieve this information from an indexing service (e.g., thatpreviously retrieved and/or indexed the data).

At step 404, process 400 (e.g., using one or more components describedabove) receives a target blockchain activity. For example, the systemmay receive a first user input selecting a target blockchain activity.For example, the system may receive a user query (e.g., via userinterface 212 (FIG. 2 )) requesting a visualization of a blockchainactivity, smart contract, and/or blockchain account. In response, thesystem may generate a visualization of this information that comprises asubset of serial blockchain activities that correspond to the targetblockchain activity, account, and/or entity.

At step 406, process 400 (e.g., using one or more components describedabove) generates a feature input. For example, the system may generate afeature input based on the blockchain activity record data and thetarget blockchain activity. The feature inputs may comprise an array ofvalues that represents the blockchain activity record data and thetarget blockchain activity.

At step 408, process 400 (e.g., using one or more components describedabove) inputs the feature input into an artificial intelligence model.For example, the system may input the feature input into an artificialintelligence model, wherein the artificial intelligence model is trainedto identify serial relationships of related blockchain activitiescorresponding to an inputted target blockchain activities based onproportions of digital assets at subsets of blockchain accounts of theplurality of blockchain accounts.

For example, the system may use the proportions of digital assets at agiven blockchain account as a metric for determining whether or not thegiven blockchain account is related to a target blockchain activity.Notably, the target blockchain activity may include a series ofintermediary activities. These intermediary activities may comprisetransactions of digital assets that are either indistinguishable fromother digital assets by their nature (e.g., the assets are fungible) ormay be indistinguishable due to the volume of related and/or unrelatedblockchain activities occurring. However, the system may identifypatterns in transactions by monitoring the balance of digital assets ata given account and determining based on that balance whether or not atransfer of digital assets corresponding to the target blockchainactivity has occurred. For example, the system may determine, using theartificial intelligence model, a respective proportion of digital assetscorresponding to each blockchain activity in the plurality of blockchainactivities. Based on the respective proportion, the system maydetermine, using the artificial intelligence model, that a firstblockchain activity is included in the subset of serial blockchainactivities.

In some embodiments, the artificial intelligence model may be furthertrained based on non-blockchain record data for a plurality of entitieslinked to one or more of the plurality of blockchain accounts, andwherein the feature input comprises information retrieved from a thirdparty source. For example, in some embodiments, the system may train amodel on a plurality of data types. For example, the system may deriverelevant/significant entities of a blockchain transaction, as well asderive the intent of the blockchain transaction sender by using acombination of, but not limited to: net balance changes of fungibleassets for all parties involved; net balance changes of nonfungibleassets for all parties involved; accounts, addresses, andcontracts/programs involved in the transaction interactions betweenaccounts, addresses, and contracts/programs involved in the transaction;public data about entities involved; and proprietary data about partiesinvolved.

In some embodiments, the system may determine that the first blockchainactivity is included in the subset of serial blockchain activities bydetermining a type of the blockchain activity. For example, the systemmay filter out certain blockchain activities that may not be indicativeof a blockchain activity being part of a larger target blockchainactivity. For example, the system may detect that a blockchain activitycorresponds to a network fee, platform fee, etc., as opposed to atransfer of digital assets between entities. In response, the system mayfilter out these types of blockchain activities. Filtering out thesetypes of blockchain activities, not only eliminates unnecessaryblockchain activities from having to be processed by the model, but alsoeliminates bias from any predictions that may be based on theseunnecessary blockchain activities.

As such, the system may determine that the first blockchain activitycorresponds to a first type of a plurality of types of blockchainactivities. The system may, in response to determining that the firstblockchain activity corresponds to the first type, retrieve a filteringcriterion for the visualization. The system may apply the filteringcriterion to the first type. The system may, in response to determiningthat the first type does not corresponds to the filtering criterion,generate for display, in the visualization, a first icon correspondingto the first blockchain activity.

In some embodiments, the system may determine that the first blockchainactivity is included in the subset of serial blockchain activities bydetermining a type of the blockchain account. For example, the systemmay monitor for blockchain accounts that share certain characteristics(e.g., accounts having a certain balance, accounts that are frequentlyused (or not used), accounts related (or otherwise linked) to one ormore entities, etc.). The system may then monitor blockchain activityacross the blockchain network for blockchain activities that are relatedto these characteristics. The system may then use the identifiedcharacteristics to find patterns in the blockchain activity across theblockchain network. The system may use these patterns to detect theintermediary blockchain activities that correspond to a targetblockchain activity. In some embodiments, detecting these patternsand/or generating visualizations that are helpful to a user may requiresome intermediary blockchain activities to be filtered out. The systemmay apply filtering criterion to do so.

As such, the system may determine that the first blockchain activitycorresponds to a first blockchain account. The system may determine thatthe first blockchain account corresponds to a first type of a pluralityof types of blockchain accounts. The system may, in response todetermining that the first blockchain account corresponds to the firsttype, retrieve a filtering criterion for the visualization. The systemmay apply the filtering criterion to the first type. The system may, inresponse to determining that the first type does not correspond to thefiltering criterion, generate for display, in the visualization, a firsticon corresponding to the first blockchain activity.

Additionally, or alternatively, the system may determine, using theartificial intelligence model, a subset of blockchain accounts of theplurality of blockchain accounts corresponding to the target blockchainactivity. The system may determine, using the artificial intelligencemodel, a respective proportion of digital assets corresponding to eachblockchain account in the subset of blockchain accounts. The system may,based on the respective proportion, determine, using the artificialintelligence model, that a first blockchain account in the subset ofblockchain accounts corresponds to the target blockchain activity.

In some embodiments, the system may also filter out activities and/oraccounts corresponding to given characteristics. For example, the systemmay, in response to determining that the first blockchain account in thesubset of blockchain accounts corresponds to the target blockchainactivity, retrieve a filtering criterion for the visualization. Thesystem may apply the filtering criterion to a characteristic of thefirst blockchain account. The system may, in response to determiningthat the characteristic does not corresponds to the filtering criterion,generate for display, in the visualization, a first icon correspondingto the first blockchain account. For example, the system may filter outcertain blockchain accounts that may not be indicative of a blockchainactivity being part of a larger target blockchain activity.

For example, the system may determine characteristics about theblockchain account or activity (e.g., frequency of use, balance, etc.)and apply filtering criteria to these characteristics. In one example,the characteristic may be based on an amount of a digital asset at issuein a blockchain activity. For example, in order to monitor only relevantblockchain activities (or blockchain activities relevant to a targetblockchain activity), the system may monitor for blockchain activitiesthat feature an amount of digital assets corresponding to the amount atissue in the target blockchain activity. In some embodiments, this maybe a percentage of that amount (or some other metric). As such, thesystem may determine an amount of a digital asset corresponding to thetarget blockchain activity. The system may then determine the filteringcriterion based on a percentage of the amount.

At step 410, process 400 (e.g., using one or more components describedabove) receives an output. For example, the system may receive an outputfrom the artificial intelligence model. For example, the output maycomprise an array of values that may be interpreted in order to generatea visualization. In some embodiments, the output may comprise one ormore predictions related to the target blockchain activity and/or aconfidence level for the one or more predictions.

At step 412, process 400 (e.g., using one or more components describedabove) generates a visualization of the target blockchain activity. Forexample, the system may generate for display, in a user interface, avisualization of the target blockchain activity based on the output,wherein the visualization comprises a subset of serial blockchainactivities that correspond to the target blockchain activity. Forexample, the visualization may comprise a text description related tothe target blockchain activity, account, and/or entity. Additionally oralternatively, the visualization may include a graphical representationof the subset of serial blockchain activities that correspond to thetarget blockchain activity, account, and/or entity. By doing so, thesystem may generate a representation of the subset of serial blockchainactivities that correspond to the target blockchain activity, account,and/or entity that would otherwise be obscured and unintuitive to auser.

In some embodiments, the visualization may include a plurality of iconscorresponding to different intermediary blockchain activities that formthe target blockchain activity. The system may allow users to selectthese icons (e.g., via user interface 212 (FIG. 2 )) in order todetermine additional information related to blockchain accounts,entities, and/or blockchain activities (e.g., an amount of digitalassets involved). For example, the visualization may comprise agraphical representation of the blockchain activities. In such cases,the system may generate for display, in the user interface, a pluralityof icons corresponding to blockchain accounts corresponding to thesubset of serial blockchain activities. The system may then generate aplurality of lines connecting the plurality of icons, wherein theplurality of lines represents characteristics of the subset of serialblockchain activities.

In some embodiments, the subset of serial blockchain activities comprisea first subset of blockchain activities on a first blockchain network,and a second subset of blockchain activities on a second blockchainnetwork. For example, the system may retrieve data from multiplesources, including multiple blockchain networks. The system may use thisinformation to determine cross-chain patterns and/or generatevisualizations featuring the cross-chain patterns.

It is contemplated that the steps or descriptions of FIG. 4 may be usedwith any other embodiment of this disclosure. In addition, the steps anddescriptions described in relation to FIG. 4 may be done in alternativeorders or in parallel to further the purposes of this disclosure. Forexample, each of these steps may be performed in any order, in parallel,or simultaneously to reduce lag or increase the speed of the system ormethod. Furthermore, it should be noted that any of the components,devices, or equipment discussed in relation to the figures above couldbe used to perform one or more of the steps in FIG. 4 .

The above-described embodiments of the present disclosure are presentedfor purposes of illustration and not of limitation, and the presentdisclosure is limited only by the claims which follow. Furthermore, itshould be noted that the features and limitations described in any oneembodiment may be applied to any embodiment herein, and flowcharts orexamples relating to one embodiment may be combined with any otherembodiment in a suitable manner, done in different orders, or done inparallel. In addition, the systems and methods described herein may beperformed in real-time. It should also be noted that the systems and/ormethods described above may be applied to, or used in accordance with,other systems and/or methods.

The present techniques will be better understood with reference to thefollowing enumerated embodiments:

-   -   1. A method comprising: receiving blockchain activity record        data for a plurality of blockchain activities involving a        plurality of blockchain accounts; receiving a first user input        selecting a target blockchain activity; generating a feature        input based on the blockchain activity record data and the        target blockchain activity; inputting the feature input into an        artificial intelligence model, wherein the artificial        intelligence model is trained to identify serial relationships        of related blockchain activities corresponding to inputted        target blockchain activities based on proportions of digital        assets at subsets of blockchain accounts of the plurality of        blockchain accounts; receiving an output from the artificial        intelligence model; and generating for display, in a user        interface, a visualization of the target blockchain activity        based on the output, wherein the visualization comprises a        subset of serial blockchain activities that correspond to the        target blockchain activity.    -   2. The method of the preceding embodiment, wherein the method is        for identifying patterns in blockchain activities based on        multi-modal data using artificial intelligence models that        compensate for training data featuring a high proportion of        missing data points.    -   3. The method of any one of the preceding embodiments, further        comprising: determining, using the artificial intelligence        model, a respective proportion of digital assets corresponding        to each blockchain activity in the plurality of blockchain        activities; and based on the respective proportion, determining,        using the artificial intelligence model, that a first blockchain        activity is included in the subset of serial blockchain        activities.    -   4. The method of any one of the preceding embodiments, wherein        determining that the first blockchain activity is included in        the subset of serial blockchain activities further comprises:        determining that the first blockchain activity corresponds to a        first type of a plurality of types of blockchain activities; in        response to determining that the first blockchain activity        corresponds to the first type, retrieving a filtering criterion        for the visualization; applying the filtering criterion to the        first type; and in response to determining that the first type        does not corresponds to the filtering criterion, generating for        display, in the visualization, a first icon corresponding to the        first blockchain activity.    -   5. The method of any one of the preceding embodiments, wherein        determining that the first blockchain activity is included in        the subset of serial blockchain activities further comprises:        determining that the first blockchain activity corresponds to a        first blockchain account; determining that the first blockchain        account corresponds to a first type of a plurality of types of        blockchain accounts; in response to determining that the first        blockchain account corresponds to the first type, retrieving a        filtering criterion for the visualization; applying the        filtering criterion to the first type; and in response to        determining that the first type does not correspond to the        filtering criterion, generating for display, in the        visualization, a first icon corresponding to the first        blockchain activity.    -   6. The method of any one of the preceding embodiments, further        comprising: determining, using the artificial intelligence        model, a subset of blockchain accounts of the plurality of        blockchain accounts corresponding to the target blockchain        activity; determining, using the artificial intelligence model,        a respective proportion of digital assets corresponding to each        blockchain account in the subset of blockchain accounts; and        based on the respective proportion, determining, using the        artificial intelligence model, that a first blockchain account        in the subset of blockchain accounts corresponds to the target        blockchain activity.    -   7. The method of any one of the preceding embodiments, further        comprising: in response to determining that the first blockchain        account in the subset of blockchain accounts corresponds to the        target blockchain activity, retrieving a filtering criterion for        the visualization; applying the filtering criterion to a        characteristic of the first blockchain account; and in response        to determining that the characteristic does not corresponds to        the filtering criterion, generating for display, in the        visualization, a first icon corresponding to the first        blockchain account.    -   8. The method of any one of the preceding embodiments, further        comprising: determining an amount of a digital asset        corresponding to the target blockchain activity; and determining        the filtering criterion based on a percentage of the amount.    -   9. The method of any one of the preceding embodiments, wherein        the artificial intelligence model is further trained based on        non-blockchain record data for a plurality of entities linked to        one or more of the plurality of blockchain accounts, and wherein        the feature input comprises information retrieved from a third        party source.    -   10. The method of any one of the preceding embodiments, wherein        generating the visualization further comprises: generating for        display, in the user interface, a plurality of icons        corresponding to blockchain accounts corresponding to the subset        of serial blockchain activities; and generating a plurality of        lines connecting the plurality of icons, wherein the plurality        of lines represent characteristics of the subset of serial        blockchain activities.    -   11. The method of any one of the preceding embodiments, wherein        the subset of serial blockchain activities comprise a first        subset of blockchain activities on a first blockchain network,        and a second subset of blockchain activities on a second        blockchain network.    -   12. A tangible, non-transitory, machine-readable medium storing        instructions that, when executed by a data processing apparatus,        cause the data processing apparatus to perform operations        comprising those of any of embodiments 1-11.    -   13. A system comprising one or more processors; and memory        storing instructions that, when executed by the processors,        cause the processors to effectuate operations comprising those        of any of embodiments 1-11.    -   14. A system comprising means for performing any of embodiments        1-11.

What is claimed is:
 1. A system for identifying malicious or other blockchain activities based on multi-modal data using artificial intelligence models that compensate for training data featuring a high proportion of missing data points, the system comprising: cloud-based storage circuitry configured to store an artificial intelligence model, wherein the artificial intelligence model is trained to identify serial relationships of related blockchain activities corresponding to inputted target blockchain activities based on proportions of digital assets at subsets of blockchain accounts of the plurality of blockchain accounts; cloud-based control circuitry configured to: receive blockchain activity record data for a plurality of blockchain activities involving a plurality of blockchain accounts, wherein the blockchain activity record data comprises first data corresponding to a first blockchain network, and second data corresponding to a second blockchain network; receive a first user input selecting a target blockchain activity; generate a feature input based on the blockchain activity record data and the target blockchain activity; input the feature input into an artificial intelligence model; and receive an output from the artificial intelligence model; and cloud-based input/output circuitry configured to generate for display, in a user interface, a visualization of the target blockchain activity based on the output, wherein the visualization comprises a subset of serial blockchain activities that correspond to the target blockchain activity, and wherein the subset of serial blockchain activities comprise a first subset of blockchain activities corresponding to the first blockchain network and a second subset of blockchain activities corresponding to the second blockchain network.
 2. A method for identifying patterns in blockchain activities based on multi-modal data using artificial intelligence models that compensate for training data featuring a high proportion of missing data points, the method comprising: receiving blockchain activity record data for a plurality of blockchain activities involving a plurality of blockchain accounts; receiving a first user input selecting a target blockchain activity; generating a feature input based on the blockchain activity record data and the target blockchain activity; inputting the feature input into an artificial intelligence model, wherein the artificial intelligence model is trained to identify serial relationships of related blockchain activities corresponding to inputted target blockchain activities based on proportions of digital assets at subsets of blockchain accounts of the plurality of blockchain accounts; receiving an output from the artificial intelligence model; and generating for display, in a user interface, a visualization of the target blockchain activity based on the output, wherein the visualization comprises a subset of serial blockchain activities that correspond to the target blockchain activity.
 3. The method of claim 2, further comprising: determining, using the artificial intelligence model, a respective proportion of digital assets corresponding to each blockchain activity in the plurality of blockchain activities; and based on the respective proportion, determining, using the artificial intelligence model, that a first blockchain activity is included in the subset of serial blockchain activities.
 4. The method of claim 3, wherein determining that the first blockchain activity is included in the subset of serial blockchain activities further comprising: determining that the first blockchain activity corresponds to a first type of a plurality of types of blockchain activities; in response to determining that the first blockchain activity corresponds to the first type, retrieving a filtering criterion for the visualization; applying the filtering criterion to the first type; and in response to determining that the first type does not corresponds to the filtering criterion, generating for display, in the visualization, a first icon corresponding to the first blockchain activity.
 5. The method of claim 3, wherein determining that the first blockchain activity is included in the subset of serial blockchain activities further comprising: determining that the first blockchain activity corresponds to a first blockchain account; determining that the first blockchain account corresponds to a first type of a plurality of types of blockchain accounts; in response to determining that the first blockchain account corresponds to the first type, retrieving a filtering criterion for the visualization; applying the filtering criterion to the first type; and in response to determining that the first type does not correspond to the filtering criterion, generating for display, in the visualization, a first icon corresponding to the first blockchain activity.
 6. The method of claim 2, further comprising: determining, using the artificial intelligence model, a subset of blockchain accounts of the plurality of blockchain accounts corresponding to the target blockchain activity; determining, using the artificial intelligence model, a respective proportion of digital assets corresponding to each blockchain account in the subset of blockchain accounts; and based on the respective proportion, determining, using the artificial intelligence model, that a first blockchain account in the subset of blockchain accounts corresponds to the target blockchain activity.
 7. The method of claim 6, further comprising: in response to determining that the first blockchain account in the subset of blockchain accounts corresponds to the target blockchain activity, retrieving a filtering criterion for the visualization; applying the filtering criterion to a characteristic of the first blockchain account; and in response to determining that the characteristic does not correspond to the filtering criterion, generating for display, in the visualization, a first icon corresponding to the first blockchain account.
 8. The method of claim 7, further comprising: determining an amount of a digital asset corresponding to the target blockchain activity; and determining the filtering criterion based on a percentage of the amount.
 9. The method of claim 2, wherein the artificial intelligence model is further trained based on non-blockchain record data for a plurality of entities linked to one or more of the plurality of blockchain accounts, and wherein the feature input comprises information retrieved from a third party source.
 10. The method of claim 2, wherein generating the visualization further comprises: generating for display, in the user interface, a plurality of icons corresponding to blockchain accounts corresponding to the subset of serial blockchain activities; and generating a plurality of lines connecting the plurality of icons, wherein the plurality of lines represent characteristics of the subset of serial blockchain activities.
 11. The method of claim 2, wherein the subset of serial blockchain activities comprise a first subset of blockchain activities on a first blockchain network, and a second subset of blockchain activities on a second blockchain network.
 12. A non-transitory, computer-readable medium, comprising instructions that, when executed by one or more processors, cause operations comprising: receiving blockchain activity record data for a plurality of blockchain activities involving a plurality of blockchain accounts; receiving a first user input selecting a target blockchain activity; generating a feature input based on the blockchain activity record data and the target blockchain activity; inputting the feature input into an artificial intelligence model, wherein the artificial intelligence model is trained to identify serial relationships of related blockchain activities corresponding to inputted target blockchain activities based on proportions of digital assets at subsets of blockchain accounts of the plurality of blockchain accounts; receiving an output from the artificial intelligence model; and generating for display, in a user interface, a visualization of the target blockchain activity based on the output, wherein the visualization comprises a subset of serial blockchain activities that correspond to the target blockchain activity.
 13. The non-transitory, computer-readable medium of claim 12, wherein the instructions further cause operations comprising: determining, using the artificial intelligence model, a respective proportion of digital assets corresponding to each blockchain activity in the plurality of blockchain activities; and based on the respective proportion, determining, using the artificial intelligence model, that a first blockchain activity is included in the subset of serial blockchain activities.
 14. The non-transitory, computer-readable medium of claim 13, wherein determining that the first blockchain activity is included in the subset of serial blockchain activities further comprises: determining that the first blockchain activity corresponds to a first type of a plurality of types of blockchain activities; in response to determining that the first blockchain activity corresponds to the first type, retrieving a filtering criterion for the visualization; applying the filtering criterion to the first type; and in response to determining that the first type does not correspond to the filtering criterion, generating for display, in the visualization, a first icon corresponding to the first blockchain activity.
 15. The non-transitory, computer-readable medium of claim 13, wherein determining that the first blockchain activity is included in the subset of serial blockchain activities further comprises: determining that the first blockchain activity corresponds to a first blockchain account; determining that the first blockchain account corresponds to a first type of a plurality of types of blockchain accounts; in response to determining that the first blockchain account corresponds to the first type, retrieving a filtering criterion for the visualization; applying the filtering criterion to the first type; and in response to determining that the first type does not corresponds to the filtering criterion, generating for display, in the visualization, a first icon corresponding to the first blockchain activity.
 16. The non-transitory, computer-readable medium of claim 12, wherein the instructions further cause operations comprising: determining, using the artificial intelligence model, a subset of blockchain accounts of the plurality of blockchain accounts corresponding to the target blockchain activity; determining, using the artificial intelligence model, a respective proportion of digital assets corresponding to each blockchain account in the subset of blockchain accounts; and based on the respective proportion, determining, using the artificial intelligence model, that a first blockchain account in the subset of blockchain accounts corresponds to the target blockchain activity.
 17. The non-transitory, computer-readable medium of claim 16, wherein the instructions further cause operations comprising: in response to determining that the first blockchain account in the subset of blockchain accounts corresponds to the target blockchain activity, retrieving a filtering criterion for the visualization; applying the filtering criterion to a characteristic of the first blockchain account; and in response to determining that the characteristic does not correspond to the filtering criterion, generating for display, in the visualization, a first icon corresponding to the first blockchain account.
 18. The non-transitory, computer-readable medium of claim 17, wherein the instructions further cause operations comprising: determining an amount of a digital asset corresponding to the target blockchain activity; and determining the filtering criterion based on a percentage of the amount.
 19. The non-transitory, computer-readable medium of claim 12, wherein the artificial intelligence model is further trained based on non-blockchain record data for a plurality of entities linked to one or more of the plurality of blockchain accounts, and wherein the feature input comprises information retrieved from a third party source.
 20. The non-transitory, computer-readable medium of claim 12, wherein generating the visualization further comprises: generating for display, in the user interface, a plurality of icons corresponding to blockchain accounts corresponding to the subset of serial blockchain activities; and generating a plurality of lines connecting the plurality of icons, wherein the plurality of lines represent characteristics of the subset of serial blockchain activities. 